Since our span-based RC tasks can be regarded as a multi-class classification problem and the span properties vary across domains, it poses more challenges for discriminators based only on features. Inspired by the Conditional Adversarial Network (CDAN) [1]}, we utilize conditional adversarial learning fusing feature \({\bf {f}}\)  and output logits \({\bf {g}}\)  for a comprehensive representation, whose network architecture is illustrated in Figure REF . It is noted that \({{\bf {f}} \in {\mathbb {R}^{m \times d}}}\)  is the BERT feature after the batch normalization layer.
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